“If the platform does not show any effort to help agents discern accurate content from inaccurate, i.e., uses a no-intervention mechanism, significant reduction in engagement can be expected,” Candogan and Drakopoulos write.
The research suggests that providing different messages about a post’s veracity to different types of users can help maximize engagement while significantly reducing fake news. The researchers identify two types of users: central users, who are connected to many friends or followers, and noncentral users, who are connected to fewer. For noncentral users, the pleasure they derive from clicking on the same things as their friends is more likely to be outweighed by a fear of engaging with misinformation. Therefore a platform can achieve high engagement levels by discouraging noncentral users from engaging with possibly inaccurate content while being more hands-off with the rest. On the other hand, platforms that make minimizing misinformation a top priority may find it beneficial to steer both types of users away from inaccurate content.
The Equation: How to signal fake news on social media
Candogan and Drakopoulos argue that companies need not bother with such distinctions if users of a network tend to have a similar number of connections. But in highly heterogeneous systems, in which users have personal networks of widely varying size, the warning levels can have a significant impact on engagement, according to the researchers.
This would apply to Facebook, where the average user has 338 friends and the median has just 200, according to the company. Twitter is even more extreme. One analyst estimated in 2013 that the median Twitter user had just one follower, compared with millions for pop star Beyoncé and US President Donald Trump.
A limitation of the research is the models’ assumption that the people using social networks and the algorithms running them know whether posts are true, false, or shaded somewhere in between. “This assumption is an approximation to reality, where a platform potentially has a more accurate estimate of the error than the agents,” Candogan and Drakopoulos write.
They suggest that further studies might look into trade-offs between promoting truth and promoting engagement in a dystopic scenario: where no one—neither networks nor users—knows what news is fake and what is real.
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